FEATUREDTechnology

Open source machine learning systems are highly vulnerable to security threats



  • MLflow identified as most vulnerable open-source ML platform
  • Directory traversal flaws allow unauthorized file access in Weave
  • ZenML Cloud’s access control issues enable privilege escalation risks

Recent analysis of the security landscape of machine learning (ML) frameworks has revealed ML software is subject to more security vulnerabilities than more mature categories like DevOps or Web servers.

The growing adoption of machine learning across industries highlights the critical need to secure ML systems, as vulnerabilities can lead to unauthorized access, data breaches, and compromised operations.



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